This dashboard communicates the divvy cyclist business patronage from 2019 to 2020 Q1:

• The Metadata Info pane shows the data structure, before and after cleaning the data set, it shows visualization of missing values, data distribution and major statistics to look at before deciding on how to further manipulate/ analyse the data set. A clean data function was written to handle the data cleaning and other feature engineering.

• The Weekly Explr pane is a weekly view of activities in the business. Finding shows that:

  • Cyclist members (subscribed) are more active than Casual members.
  • Casual members Average Trip duration is higher than subscribed member.
  • The gender participation shows that Men are active than Women but their average trip duration are similar on some days.

• The Monthly Explr pane is a monthly view of activities in the business. Finding shows that:

  • Cyclist members (subscribed) are more active than Casual members, but their trends looks similar on a monthly view.
  • Also Casual members Average Trip duration is higher but with a little gap, except for January and February.
  • The gender participation shows that Men are active than Women but their average trip duration are similar on some days, Also, Women activities were missing or not on record from April to June, Questions needs to be directed to the operation team.

• The Quarterly Explr pane is a Quarterly view of activities in the business. Finding shows that:

  • Cyclist members (subscribed) are more active than Casual members, by a large trendon quarter view.
  • But Casual members Average Trip duration is higher than all subscribed members, this confirms the weekly activity report.
  • The gender participation shows that Men are active than Women but their average trip duration are very close on some days. Also, Women activities were missing or not on record in Q2, Questions needs to be directed to the operation team.

• Activity Forecast pane shows the average trip duration and daily patronage forecast of business operation up to 12 months and 365 days respectively.

  • The average trip duration forecast from the MAE implies that the predictions of the model are off by about 199.75 trip duration per day. While the RMSE indicates that the standard deviation of the prediction errors (residuals) is about 265.38 trip duration day.

  • The daily patronage forecast from the MAE implies that the predictions of the model are off by about 2219.75 avg daily rides per day. While the RMSE indicates that the standard deviation of the prediction errors (residuals) is about 2796.20 avg daily rides per day.

  • If we are planning resources (like the number of bikes available), business might need to consider these errors to ensure they don’t under- or over-estimate demand.